CycleGAN operator generates images for domain transfer based on CycleGAN, that is, converts one type of images into another, or converts samples in the X space into samples in the Y space. CycleGAN can use non-paired data for training. During model training, two inputs are supported, indicating the source domain and target domain of data, respectively. After the training is complete, all images transferred from the source domain to the target domain are generated.
Name |
Default |
Description |
|---|---|---|
do_validation |
True |
Whether to validate data. The default value is True, which indicates that data is validated before being generated. False indicates data is generated directly. |
image_channel |
3 |
Number of channels of the image |
image_height |
256 |
Image height. The value must be 2 to the power of n. |
image_width |
256 |
Image width. The value must be 2 to the power of n. |
batch_size |
1 |
Number of samples for batch training |
max_epoch |
100 |
Number of dataset epochs during training |
g_learning_rate |
0.0001 |
Learning rate for the generator training |
d_learning_rate |
0.0001 |
Learning rate for the discriminator training |
log_frequency |
5 |
Logging frequency (counted by step) |
save_frequency |
5 |
Model save frequency (counted by epoch) |
predict |
False |
Whether to perform inference and prediction. The default value is False. If this parameter is set to True, you need to set resume to the OBS path where the trained model is stored. |
resume |
empty |
If predict is set to True, enter the OBS path where the TensorFlow model file is stored for inference and prediction. Currently, only models in .pb format are supported. Example: obs://xxx/xxxx.pb. The default value is empty. |
The following two types of operator input are available:
The single-level directory structure is as follows:
image_folder----0001.jpg
----0002.jpg
----0003.jpg
...
----1000.jpg
The dual-level directory structure is as follows:
image_folder----sub_folder_1----0001.jpg
----0002.jpg
----0003.jpg
...
----0500.jpg
----sub_folder_2----0001.jpg
----0002.jpg
----0003.jpg
...
----0500.jpg
...
----sub_folder_100----0001.jpg
----0002.jpg
----0003.jpg
...
----0500.jpg
The output directory structure is as follows: The folder model stores model frozen PB for inference, the folder samples stores the output images during training, and the folder Data stores the images generated by the training model.
train_url----model----CYcleGan_epoch_10.pb
----CYcleGan_epoch_20.pb
...
----CYcleGan_epoch_1000.pb
----samples----0000_0.jpg
----0000_1.jpg
...
----0100_15.jpg
----Data----CYcleGan_0_0.jpg
----CYcleGan_0_1.jpg
...
----CYcleGan_16_8.jpg
----output_0.manifest
A manifest file example is as follows:
{
"id": "xss",
"source": "obs://home/fc8e2688015d4a1784dcbda44d840307_14.jpg",
"usage": "train",
"annotation": [
{
"name": "Cat",
"type": "modelarts/image_classification"
}
]
}